Ani4K is a 2x model for creating natural-looking 2K and 4K upscales of modern anime. The model places emphasis on depth of field and blur effect retention, which are extremely common in modern anime and something that many anime models struggle with. In addition, Ani4K is trained for strong detail retention, WEB/BD compression clean-up, and producing pleasing lineart. 

More info on the Github page: https://github.com/Sirosky/Sirosky-Upscaling-Models.

Blur Retention Comparisons (including vs. xinntao's 2x paper model): https://imgsli.com/MTg2NTg5/4/5

General Comparisons: https://imgsli.com/MTg2ODI2

Ani4K

Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K. 

The aim of these models is to address scaling, blur, oversharpening, and compression artifacts while upscaling to deliver a result that appears as if the anime was originally mastered in 4K resolution. Can be set up to run in real-time with mpv on Windows using https://github.com/the-database/mpv-upscale-2x_animejanai

The development of the V3 models spanned over seven months, during which over 100 release candidate models were trained and meticulously refined. The V3 models introduce several improvements compared to V2, including:

More faithful appearance to original source
Improved handling of oversharpening artifacts, ringing, aliasing
Better at preserving intentional blur in scenes using depth of field
More accurate line colors, darkness, and thickness
Better preservation of soft shadow edges

Overall, the V3 models yield significantly more natural and faithful results compared to the V2 models.test

AnimeJaNai_HD_V3_Compact

Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K.

The aim of these models is to address scaling, blur, oversharpening, and compression artifacts while upscaling to deliver a result that appears as if the anime was originally mastered in 4K resolution. Can be set up to run in real-time with mpv on Windows using https://github.com/the-database/mpv-upscale-2x_animejanai

The development of the V3 models spanned over seven months, during which over 100 release candidate models were trained and meticulously refined. The V3 models introduce several improvements compared to V2, including:

More faithful appearance to original source Improved handling of oversharpening artifacts, ringing, aliasing Better at preserving intentional blur in scenes using depth of field More accurate line colors, darkness, and thickness Better preservation of soft shadow edges

Overall, the V3 models yield significantly more natural and faithful results compared to the V2 models.

AnimeJaNai_HD_V3_SuperUltraCompact

AnimeJaNai_HD_V3_UltraCompact

GitHub Link: https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set

This is my first anime model in years. Hopefully you guys can find a good use-case for it. Included are 4 models:

1. RealPLKSR (Higher quality, slower)
2. MoSR (Lower quality, faster)

There are Sharp and Soft versions of both
When to use each:
- __Sharp:__ For heavily degraded sources. Sharp models have issues depth of field but are best at removing artifacts 
- __Soft:__ For cleaner sources. Soft models preserve depth of field but may not remove other artifacts as well

__Notes:__
- MoSR doesn't work in chaiNNer currently
- To use MoSR:
  1. Use the ONNX version in tools like [VideoJaNai](<https://github.com/the-database/VideoJaNai>)
  2. Update spandrel in the latest version of ComfyUI

The ONNX version may produce slightly different results than the .pth version. If you have issues, try the .pth model.

2x-AnimeSharpV2_MoSR_Sharp

2x-AnimeSharpV2_MoSR_Soft

Purpose: Anime

This is a successor to AnimeSharpV3 based on RCAN instead of ESRGAN. It outperforms both versions of AnimeSharpV3 in every capacity. It's sharper, retains *even more* detail, and has very few artifacts. It is __extremely faithful__ to the input image, even with heavily compressed inputs. 

To use this model, you must update to the [**latest chaiNNer nightly build**](https://github.com/chaiNNer-org/chaiNNer-nightly/releases)

The `2x-AnimeSharpV4_Fast_RCAN_PU` model is trained on RCAN PixelUnshuffle. This is much faster, but comes at the cost of quality. I believe the model is ~95% the quality of the full V4 RCAN model, but ~6x faster in Pytorch and ~4x faster in TensorRT. This model is ideal for video processing, and as such was trained to handle MPEG2 & H264 compression.

__Comparisons:__ https://slow.pics/c/63Qu8HTN
https://slow.pics/c/DBJPDJM9
![1736292155 679079](https://github.com/user-attachments/assets/875be696-0948-4de2-9d03-85a581891a49)

AnimeSharpV4

AnimeSharpV4_Fast_RCAN_PU

Purpose: 2x anime upscaler which tries to preserve dof

2x anime upscaler, a non-otf version of the HFA2kCompact model which tries to preserve dof by not deblurring.

Original Link: https://drive.google.com/drive/folders/1RZbALUTCf4DYDTVMBR74b8Nt-7m6TEyk?usp=drive_link

HFA2k compact multijpg

2x SRFormer_light anime upscale model that handles AVC (h264) compression since h264 crf 20-28 degradation together with bicubic, bilinear, box and lanczos downsampling has been applied on musl's HFA2k dataset with Kim's dataset destroyer for training.


If you want to run this model with chaiNNer (or another application) you need to use the onnx files with an onnx upscale node. All onnx conversions can be found in the [onnx folder on my repo](https://github.com/Phhofm/models/tree/main/2xHFA2kAVCSRFormer_light/onnx).

Example 1: https://imgsli.com/MTkxMTQz

Example 2: https://imgsli.com/MTkxMTQ0

2xHFA2kAVCSRFormer_light

Upscaling frames from Irodori anime (namely kemono friends) from 540p (the source render resolution) to 1080p, low resolution flat shaded art, de-JPEG of the aforementioned

KemonoScale v2

Upscale Anime while keeping as much of the original detail as possible. Isn't among the sharpest models and if the anime is somewhat blurry it will retain that detail but add more pixels to give a smoother edge. Doesn't change the color very much and may make some scene's very slightly darker.

Pretrained model: Pony Models

Loyaldk Giroro

Loyaldk Keroro

Loyaldk Kororo

Upscale MLP episodes. Liter version. Able to handle compression better compared to V1.0 and no longer creates halo's/rainbows. Good for Vector 2D art however converts detail to blobs.

Loyaldk LitePony V2.0

Upscale MLP episodes. Liter version. Able to handle compression better compared to V1.0 and no longer creates halo's/rainbows. Good for Vector 2D art however converts detail to blobs. Leaves more blobs when converting detail to blobs compared to litepony

Loyaldk MediumPony V2.0

Upscale MLP episodes. Full version. Able to handle compression better compared to V1.0 and no longer creates halo's/rainbows. Good for Vector 2D art however converts detail to blobs.

Loyaldk SuperPony V2.0

Made to restore screentones and remove compression artifacts in manga images with widths between 650 and 900 pixels (can be more than 900).

2x Manga Ora

2x compact fast universal upscaler pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

2x-NomosUni_compact_multijpg

Purpose: 2x fast universal upscaler with medium degradation handling (jpg compression, noise, blur)

2x compact fast universal upscaler with medium degradation handling using the Real-ESRGAN training pipeline, based off 2xNomosUni_compact_otf_strong. Handles jpg compression, some noise, and some blur (so dejpgs, denoises and deblurs).

Original Link: https://drive.google.com/drive/folders/1YdOPyb0Ht9YjoRZILf87hSocyAx43CLc

Examples:
[RealPhoto](https://imgsli.com/MjMyNTUy)
[Noisy](https://imgsli.com/MjMyNTUz)

NomosUni compact otf medium

Purpose: 2x universal upscaler

2x esrgan universal upscaler, pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

Original Link: https://drive.google.com/drive/folders/12zKVS74mz0NtBKGlkx_r0ytUVoeDiTX3?usp=drive_link

Try it out in [this huggingface space](https://huggingface.co/spaces/Phips/upscale) I made (since the spaces uses cpu, testing locally on your gpu would be faster)

Slow Pics crops examples:
[ani](https://slow.pics/c/LLP9p89w)
[face](https://slow.pics/c/4IsxVMAB)
[dof_wheat](https://slow.pics/c/zj4q6Mvi)

NomosUni esrgan multijpg

2x span fast universal upscaler pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

2x-NomosUni_span_multijpg

Purpose: 2x fast universal DoF preserving upscaler

2x span fast universal DoF preserving upscaler, pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

Original Link: https://drive.google.com/drive/folders/1_P56B3k1XrBsa-f698zPHAuLaqQ8sSWF

Examples:
[dofwheat](https://slow.pics/c/k0U59uJC)
[digitalart](https://slow.pics/c/yyY0xMAn)  
[face](https://slow.pics/c/MdV3Lr7N)

NomosUni span multijpg ldl

4x anime image upscaler with a bit of otf jpg compression and blur. Trained on musl's hfa2k dataset release for Anime SISR, which has been extracted from modern anime films, where the selection criteria was high SNR, no DOF and high frequency information.

Examples: https://imgsli.com/MTc2NDgx (PS Example input images are small, each around 500x280 px) \
Example input files: https://drive.google.com/drive/folders/1RI6gGqRy-KxDujbaIrpEMdvFkIWHvfPt \
Example output files: https://drive.google.com/drive/folders/1GqHwPlFp6bIQl4R1AxmrmJ6vUUD8FUxH \
All my model files can be found on my github repo https://github.com/Phhofm/models

HFA2k

4x fast universal upscaler pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

Architecture	Compact
Scale	2x
Color Mode	RGB
License	CC-BY-4.0 Private use Commercial use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2023-12-13
Dataset	Nomos_Uni
Dataset size	2989
Training iterations	30000
Training epochs	17
Training batch size	9
Training HR size	512
Training OTF	No

2x-NomosUni_compact_multijpg

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